plan 인풋 summary 추가 및 firecrawl 캐싱 삭제
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@ -16,7 +16,7 @@ class FirecrawlClient:
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HTTPMethod.POST,
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url=f"{FIRECRAWL_BASE}/scrape",
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headers=self._headers(),
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json_body={"url": url, "formats": ["json", "links"], "jsonOptions": json_options, "waitFor": wait_for},
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json_body={"url": url, "formats": ["json", "links"], "jsonOptions": json_options, "waitFor": wait_for, "maxAge": 0},
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label="firecrawl-scrape",
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)
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if not resp or not resp.is_success:
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@ -76,7 +76,7 @@ class FirecrawlClient:
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"url": url,
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"formats": ["json", "links", "rawHtml"],
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"jsonOptions": {
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"prompt": "Extract: clinic name (Korean), clinic name (English), address, phone with dash format, business hours, slogan, services offered, doctors with name/title/specialty, brand identity (primary/accent/background/text colors in hex, heading/body fonts, logo URL from the actual header/main <img> src, og:image from <meta property='og:image'> content, favicon URL)",
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"prompt": "Extract: 클리닉 이름 - clinicName (Korean), clinic name (English), address, phone with dash format, business hours, slogan, services offered, doctors with name/title/specialty, brand identity (primary/accent/background/text colors in hex, heading/body fonts, logo URL from the actual header/main <img> src, og:image from <meta property='og:image'> content, favicon URL)",
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"schema": {
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"type": "object",
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"properties": {
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@ -127,6 +127,7 @@ class FirecrawlClient:
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},
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},
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"waitFor": 5000,
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"maxAge": 0,
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},
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timeout=60,
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label="firecrawl-clinic-info",
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@ -187,6 +188,7 @@ class FirecrawlClient:
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},
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},
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"waitFor": 5000,
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"maxAge": 0,
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},
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timeout=60,
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label="firecrawl-gangnamunni",
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@ -13,7 +13,7 @@ from integrations.llm.schemas.report import (
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ScoresInput, ScoresOutput,
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OtherChannelsInput, OtherChannelsOutput
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)
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from integrations.llm.schemas.plan import PlanInput, PlanOutput
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from integrations.llm.schemas.plan import PlanInput, PlanOutput, SummarizeInput, SummarizeOutput
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from integrations.llm.schemas.market import (
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MarketCompetitorsInput, MarketCompetitorsOutput,
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MarketKeywordsInput, MarketKeywordsOutput,
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@ -64,6 +64,13 @@ plan_prompt = Prompt(
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output_class=PlanOutput,
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)
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summarize_prompt = Prompt(
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file_name="summarize_prompt.txt",
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prompt_model="PLAN_MODEL",
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input_class=SummarizeInput,
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output_class=SummarizeOutput,
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)
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market_competitors_prompt = Prompt(
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file_name="market_competitors_prompt.txt",
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prompt_model="MARKET_MODEL",
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@ -2,6 +2,15 @@ from typing import Literal
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from pydantic import BaseModel
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class SummarizeInput(BaseModel):
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label: str
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data: str
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class SummarizeOutput(BaseModel):
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summary: str
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class PlanInput(BaseModel):
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clinic_name: str | None = None
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clinic_name_en: str | None = None
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@ -0,0 +1,7 @@
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다음은 "{label}" 원본 데이터입니다.
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{data}
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위 데이터를 마케팅 플랜 생성에 필요한 핵심 정보만 남기고 간결하게 요약하세요.
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구체적인 수치·날짜·고유명사(채널명, 게시물 제목 등)는 그대로 보존하고, 중복되거나 플랜 작성에 불필요한 메타데이터는 제거하세요.
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요약문 하나의 문자열로만 출력하세요.
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@ -1,3 +1,4 @@
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import asyncio
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import json
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import logging
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from urllib.parse import urlparse
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@ -7,7 +8,7 @@ from common.db.run import update_run_report, update_run_plan, select_run_report_
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from common.db.source import select_run_raw_data, select_mainpage_logo_url
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from common.db.market import select_market
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from integrations.llm.llm_service import LLMService
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from integrations.llm.prompt import report_prompt, plan_prompt, youtube_diagnosis_prompt, brand_consistency_prompt, critical_issues_prompt, transformation_prompt, roadmap_prompt, scores_prompt, other_channels_prompt
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from integrations.llm.prompt import report_prompt, plan_prompt, summarize_prompt, youtube_diagnosis_prompt, brand_consistency_prompt, critical_issues_prompt, transformation_prompt, roadmap_prompt, scores_prompt, other_channels_prompt
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from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit, BrandConsistencyOutput, CriticalIssuesOutput, DiagnosisItem, TransformationProposal, RoadmapOutput, RoadmapMonth, ScoresOutput, ChannelScore, WebsiteAudit, OtherChannelsOutput, OtherChannel
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from services.branding import analyze_branding
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from services.instagram_audit import build_instagram_audit
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@ -49,14 +50,8 @@ async def generate_plan(analysis_run_id: str) -> PlanOutput:
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def _json(v) -> str | None:
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return json.dumps(v, ensure_ascii=False) if v else None
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input_data = {
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"clinic_name": clinic.get("clinicName"),
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"clinic_name_en": clinic.get("clinicNameEn"),
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"address": clinic.get("address"),
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"phone": clinic.get("phone"),
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"slogan": clinic.get("slogan"),
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"services": json.dumps(clinic.get("services", []), ensure_ascii=False),
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"doctors": json.dumps(clinic.get("doctors", []), ensure_ascii=False),
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# map: 큰 입력은 LLM으로 압축 요약해 100KB 초과 에러를 방지하고, 작은 입력은 그대로 둔다.
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large_fields = {
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"report": _json(report),
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"market_competitors": _json(market.get("competitors")),
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"market_keywords": _json(market.get("keywords")),
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@ -65,16 +60,42 @@ async def generate_plan(analysis_run_id: str) -> PlanOutput:
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"tiktok": _json(tiktok),
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"instagram": _json(instagram),
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"facebook": _json(facebook),
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"naver_blog": _json(_naver_blog_summary(naver_blog)),
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"naver_cafe": _json(naver_cafe),
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"kakao_talk": _json(kakaotalk),
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"channel_logos": _json(branding.get("channelLogos")),
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"brand_assets": _json(branding.get("brandAssets")),
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}
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summarized = dict(zip(
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large_fields.keys(),
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await asyncio.gather(*(_summarize(label, data) for label, data in large_fields.items())),
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))
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# reduce: 요약된 입력을 모아 최종 플랜 생성.
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input_data = {
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"clinic_name": clinic.get("clinicName"),
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"clinic_name_en": clinic.get("clinicNameEn"),
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"address": clinic.get("address"),
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"phone": clinic.get("phone"),
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"slogan": clinic.get("slogan"),
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"services": json.dumps(clinic.get("services", []), ensure_ascii=False),
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"doctors": json.dumps(clinic.get("doctors", []), ensure_ascii=False),
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"naver_blog": _json(_naver_blog_summary(naver_blog)),
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"kakao_talk": _json(kakaotalk),
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**summarized,
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}
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return await LLMService(provider="perplexity").generate(plan_prompt, input_data)
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_SUMMARIZE_THRESHOLD = 4000 # 이 길이(문자 수)를 넘는 입력만 요약 LLM 호출 (불필요한 호출 방지)
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async def _summarize(label: str, data: str | None) -> str | None:
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if not data or len(data) <= _SUMMARIZE_THRESHOLD:
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return data
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result = await LLMService(provider="perplexity").generate(summarize_prompt, {"label": label, "data": data})
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return result.summary
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def _build_clinic_snapshot(mainpage: dict, gangnam_unni: dict, brand_assets: dict, logo_url: str | None) -> dict:
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snapshot: dict = {}
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doctors = gangnam_unni.get("doctors", [])
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